Fraud Detection of Bulk Cargo Theft in Port Using Bayesian Network Models

Publisher's version (útgefin grein)The fraud detection of cargo theft has been a serious issue in ports for a long time. Traditional research in detecting theft risk is expert- and survey-based, which is not optimal for proactive prediction. As we move into a pervasive and ubiquitous paradigm, the implications of external environment and system behavior are continuously captured as multi-source data. Therefore, we propose a novel data-driven approach for formulating predictive models for detecting bulk cargo theft in ports. More specifically, we apply various feature-ranking methods and classification algorithms for selecting an effective feature set of relevant risk elements. Then, implicit Bayesian networks are derived with the features to graphically present the relationship with the risk elements of fraud. Thus, various binary classifiers are compared to derive a suitable predictive model, and Bayesian network performs best overall. The resulting Bayesian networks are then comparatively analyzed based on the outcomes of model validation and testing, as well as essential domain knowledge. The experimental results show that predictive models are effective, with both accuracy and recall values greater than 0.8. These predictive models are not only useful for understanding the dependency between relevant risk elements, but also for supporting the strategy optimization of risk management."Peer Reviewed

"Rotterdam econometrics": publications of the econometric institute 1956-2005

This paper contains a list of all publications over the period 1956-2005, as reported in the Rotterdam Econometric Institute Reprint series during 1957-2005.

Incorporation of deficiency data into the analysis of the dependency and interdependency among the risk factors influencing port state control inspection

Port State Control (PSC) inspection aids to control substandard ships and ensure safety at sea. Current risk-based PSC research and practice fail to incorporate ship deficiency records into detention probability analysis, because of the difficulty introduced by the involved big deficiency data. In this paper, a new Bayesian Network (BN) based PSC risk probabilistic model is developed to analyze the dependency and interdependency among the risk factors influencing PSC inspections based on big data derived from the inspection database of Tokyo MoU for the period between 2014 and 2017. The results reveal that ship's safety condition related deficiencies as well as technical features of the inspected vessel itself are among the most influential factors concerning PSC inspections and ship detention. New Bayesian learning methods are used to improve the model efficiency in ship detention prediction. As a result, the newly developed model has shown a reliable performance on dynamic prediction and cause-effect diagnosis of ship detention probabilities by pioneering the incorporation of ship deficiency records in the analysis. The findings provide important insights on how to facilitate risk-based PSC inspections for both ship owners and port states. They provide support for port state authorities to implement rational inspection policies

Modeling and Measuring Resilience: Applications in Supplier Selection and Critical Infrastructure

Nowadays, infrastructure systems such as transportation, telecommunications, water supply, and electrical grids, are considerably facing the exposure of disruptive events such as natural disasters, manmade accidents, malevolent attacks, and common failures due to their size, complexity, and interconnectedness nature. For example fragile design of supply chain infrastructure might collapses because the consequences of a failure can propagate easily through the layers of supply chains, especially for large interconnected networks. Previously, owners and operators of infrastructure systems focused to design cost-efficient, competitive and sustainable ones; however the need for design of resilient infrastructure systems is inevitable. Infrastructure systems must be designed in such a way so that they are resistant enough to withstand and recover quickly from disruptions. The consequences of disruptive events on infrastructures ranging from energy systems (e.g., electrical power network, natural gas pipeline) to transportation systems (e.g., food supply chain, public transportation) cannot only impacted on individuals, but also on communities, governments and economics. The goal of this dissertation is to (i) identify the resilience capacities of infrastructure systems; in particular inland waterway ports, and supply chain systems, (ii) quantify and analyze the resilience value of critical infrastructure systems (CIs), (iii) improve the resilience of CIs by simulating different disruptive scenarios, and (iv) recommend managerial implications to help owners and operators of CIs for timely response, preparedness, and quick recovery against disruptive events. This research first identifies the resilience capacity of CIs, in particular, inland waterway, supply chain and electrical power plant. The resilience capacity of CIs is modeled in terms of their absorptive capacity, adaptive capacity and restorative capacity. A new resilience metric is developed to quantify the resilience of CIs. The metric captures the causal relationship among the characteristics of CIs and characteristics of disruptive events including intensity and detection of disruption likelihood of disruptive events. The proposed resilience metric is generic, meaning that can be applied across variety of CIs. The proposed metric measures the system resilience as the sum of degree of achieving successful mitigation and contingency strategies. The resilience metric accounts for subjectivity aspect of disruptive events (e.g., late disruption detection, very intense disruption, etc.). Additionally, the proposed resilience metric is capable of modeling multiple disruptive events occurring simultaneously. This research study further explores how to model the resilience of CIs using graphical probabilistic approach, known as Bayesian Networks (BN). BN model is developed to not only quantify the resilience of CIs but also to predict the behavior of CIs against different disruptive scenarios using special case of inference analysis called forward propagation analysis (FPA), and improvement scenarios on resilience of CIs are examined through backward propagation analysis (BPA), a unique features of BN that cannot be implemented by any other methods such as classical regression analysis, optimization, etc. Of interest in this work are inland waterway ports, suppliers and electrical power plant. Examples of CIs are inland waterway ports, which are critical elements of global supply chain as well as civil infrastructure. They facilitate a cost-effective flow of roughly $150 billion worth of freights annually across different industries and locations. Stoppage of inland waterway ports can poses huge disruption costs to the nation’s economic. Hence, a series of questions arise in the context of resilience of inland waterway ports. How the resilience of inland waterway ports can be modeled and quantified? How to simulate impact of potential disruptive events on the resilience of inland waterway ports? What are the factors contributing to the resilience capacity of inland waterway ports? How the resilience of inland waterway can be improved

"Rotterdam econometrics": publications of the econometric institute 1956-2005

This paper contains a list of all publications over the period 1956-2005, as reported in the Rotterdam Econometric Institute Reprint series during 1957-2005

Модели и методы машинного обучения для решения задач оптимизации и прогнозирования работы морских портов

Machine learning techniques have made significant advances and expanded application sphere over the past decade to include problems of port operations. This happened due to the growing amount of data available cargo ports. We review the literature on models and methods of machine learning and their application to optimization of port operations. A special attention is paid to the port planning and development a wide range of topics in port operations, including port planning and development, their safety and security, water and land port operations.За последнее десятилетие существенно улучшились методы машинного обучения и расширилась сфера их применения, которая дополнилась рядом операционных задач, возникающих в грузовых портах. Это связано с накоплением и возможностью использования имеющихся в грузовых портах больших объемов данных. Статья посвящена обзору литературы по моделям и методам машинного обучения и их применению к оптимизации портовых операций. Основное внимание уделено планированию и развитию портов, их безопасности и охране, водным и сухопутным портовым операциям

Privacy-Preserving Mining of Web Service Conversations

Organizations and businesses are exporting their applications as Web services seeking more collaboration opportunities. These services are generally not used in silos. Indeed, the invocation of a service is often conditioned by the invocation of other services. We refer to the precedence relationships between service invocations as conversations or choreographies. As clients interact with Web services, they exchange an important quantity of sensitive data, hence raising the challenge to keep the privacy of various interactions. In addition to the data exchanged with Web services, users may consider the information about service usage as sensitive and would like to hide that information from third parties. However, conversation relationships may complicate the task of keeping such information secret. In this Thesis, we extend the traditional concept of k-anonymity introduced for databases to Web service conversations. The goal is to determine the extent to which the invocation of a service can be inferred from downstream invocations. We first use the FP-Growth algorithm for mining service invocation logs. The mining process returns the probabilities of service conversations. We then define a probabilistic k-anonymity technique for Web service conversations based on the results of the mining process. The proposed approach assists users in selecting Web services that best satisfy their anonymity requirements. We conducted extensive experiments using realworld Web services to prove the efficiency of the proposed approach.Master of ScienceComputer and Information Science, College of Engineering and Computer ScienceCollege of Engineering and Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/138104/1/Privacy-Preserving Mining of Web Service Conversations.pdfDescription of Privacy-Preserving Mining of Web Service Conversations.pdf : Thesi

Methods and Applications of Synthetic Data Generation

The advent of data mining and machine learning has highlighted the value of large and varied sources of data, while increasing the demand for synthetic data captures the structural and statistical characteristics of the original data without revealing personal or proprietary information contained in the original dataset. In this dissertation, we use examples from original research to show that, using appropriate models and input parameters, synthetic data that mimics the characteristics of real data can be generated with sufficient rate and quality to address the volume, structural complexity, and statistical variation requirements of research and development of digital information processing systems. First, we present a progression of research studies using a variety of tools to generate synthetic network traffic patterns, enabling us to observe relationships between network latency and communication pattern benchmarks at all levels of the network stack. We then present a framework for synthesizing large scale IoT data with complex structural characteristics in a scalable extraction and synthesis framework, and demonstrate the use of generated data in the benchmarking of IoT middleware. Finally, we detail research on synthetic image generation for deep learning models using 3D modeling. We find that synthetic images can be an effective technique for augmenting limited sets of real training data, and in use cases that benefit from incremental training or model specialization, we find that pretraining on synthetic images provided a usable base model for transfer learning